Learning curves for environmental technology and their importance for climate policy analysis

125 citations recorded by the source corpus.

How researchers see this paper

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The dynamics of behavior change: Evidence from energy conservation (2016, background): “While the research so far has emphasized macro effects on the diffusion of greener technologies (Bollinger and Gillingham, 2012; Rubin et al., 2004), our research demonstrates the advantages of a more micro and dynamic approach to understanding consumer responses to innovation and…”
Options and Instruments for a Deep Cut in CO2 Emissions: Carbon Dioxide Capture or Renewables, Taxes or Subsidies? (2006, background): “8 Worldwide, the costs of applying these technologies have decreased considerably over the past few decades (Rubin et al., 2004) and learning rates for capital costs of 11-12% have been observed.”
Economics of geological CO2 storage and leakage (2009, methodology): “As for the prospected cost reduction potential of CCS technology we follow the current learning curve literature and adopt a value of 10% for the corresponding learning rate (IEA/OECD 2000; McDonald and Schrattenholzer 2001; Rubin et al. 2004).”
Interim monitoring of cost dynamics for publicly supported energy technologies (2009, background): “And for those studies that use experience curves to represent technological change, assumptions about LRs are important (Rubin et al., 2004; Kahouli-Brahmi, 2008).”